AuctionRouter_PRD.md

Product Requirements Document (PRD)

AuctionRouter: Cost-Aware Multi-Agent LLM Orchestrator inspired by https://arxiv.org/pdf/2607.09600

1. Overview

AuctionRouter is a multi-agent AI system that minimizes inference cost while maintaining answer quality by routing requests through a hierarchy of language models.

Instead of sending every request to an expensive frontier model, the system:

1. Uses multiple low-cost models to evaluate a task.
2. Runs an auction-based selection process.
3. Generates an answer using the selected low-cost model.
4. Uses a verifier model to evaluate answer quality.
5. Escalates to a frontier model only when confidence is insufficient or verification fails.

The goal is to achieve:

• 60-80% lower inference cost
• Lower latency
• Comparable answer quality to frontier-only systems

---

2. Problem Statement

Pattern A

User → GPT-5

Pros

• High quality

Cons

• Expensive
• Slow

Pattern B

User → Cheap Model

Pros

• Fast
• Cheap

Cons

• Lower quality
• Hallucinations

The ideal system should:

• Use cheap models whenever possible
• Detect when cheap models are insufficient
• Escalate only when necessary

---

3. Goals

Primary Goals

• Reduce average cost per query
• Maintain answer quality
• Demonstrate agent orchestration
• Visualize model routing decisions

Secondary Goals

• Collect model performance data
• Compare models over time
• Provide explainable routing

---

4. User Personas

AI Engineer

Wants to understand model routing and optimization.

Recruiter

Wants to see practical multi-agent engineering.

Developer

Wants cheaper inference than GPT-only solutions.

---

5. System Architecture

User Query
    │
    ▼
Query Analyzer
    │
    ▼
Auction Manager
 ┌──────┼──────┐
 ▼      ▼      ▼
Gemini DeepSeek Qwen
 └──────┼──────┘
        ▼
 Bid Aggregation
        ▼
  Winner Model
        ▼
   Draft Answer
        ▼
  Verifier Agent
     │      │
   Pass   Fail
     │      │
 Return  Escalate
             ▼
      GPT-5 / Claude
             ▼
        Final Answer

---

6. Models

Tier 1 (Open and Free Models)

• Gemini Flash
• DeepSeek
• Qwen

Responsibilities:

• Bid on tasks
• Generate low-cost answers

Tier 2 (Frontier Models)

• GPT-5
• Claude Sonnet

Responsibilities:

• Complex reasoning
• Escalated requests

Verifier Model (bigger but free model)

Responsibilities:

• Evaluate correctness
• Evaluate completeness
• Detect hallucinations
• Decide whether escalation is required

Potential choices:

• Gemini Flash
• GPT-5 Nano
• Qwen-based verifier

---

7. Auction Mechanism

Each cheap model receives the user query and returns:

{
  "confidence": 0.87,
  "estimated_difficulty": 0.65,
  "reason": "Strong at coding tasks"
}

Auction Score

Auction Score =
0.7 × Confidence
+ 0.2 × Historical Accuracy
- 0.1 × Cost

The model with the highest score generates the draft answer.

---

8. Verification System

After answer generation, the verifier receives:

• Original question
• Generated answer

The verifier evaluates:

1. Correctness
2. Completeness
3. Reasoning quality
4. Hallucination risk

Returns:

{
  "score": 0.84,
  "pass": true,
  "feedback": "Answer appears correct."
}

Verification Threshold

score >= 0.80

If the answer fails verification, the request is escalated.

---

9. Escalation Logic

Condition 1

Low auction confidence:

max_confidence < 0.75

Condition 2

Verifier failure:

verification_score < 0.80

Condition 3

Strong model disagreement:

Gemini  = 0.90
DeepSeek = 0.41
Qwen     = 0.37

High variance triggers escalation.

---

10. Frontend

Chat Interface

Simple chat experience for submitting queries.

Auction Visualization

Display:

• Model confidence
• Bid score
• Cost estimate
• Winner selection

Verification Panel

Display:

• Verification score
• Pass / Fail status
• Escalation reason

Routing Graph

Visualize the path:

Query
  ↓
Auction
  ↓
Winner
  ↓
Verifier
  ↓
Response

or

Query
  ↓
Auction
  ↓
Winner
  ↓
Verifier
  ↓
GPT-5 / Claude
  ↓
Response

---

11. Metrics Dashboard

Track:

Cost Metrics

• Average cost per query
• Total cost saved

Latency Metrics

• Average response time

Escalation Metrics

• Escalation percentage
• Tier-1 resolution rate

Model Metrics

• Gemini wins
• DeepSeek wins
• Qwen wins

---

12. Tech Stack

Frontend

• Next.js
• React
• Tailwind
• shadcn/ui
• React Flow

Backend

• FastAPI
• LangGraph

Model Access

• OpenRouter

Database

-Mongo

Observability

• LangSmith

Deployment (done after testing locally)

• Vercel

---

13. Success Metrics

Target outcomes:

• Tier-1 Resolution Rate > 70%
• Cost Reduction > 60%
• Latency Reduction > 30%
• Escalation Rate < 30%

---

14. Resume Value

This project demonstrates:

• Multi-agent systems
• Agent orchestration
• LLM routing
• Model evaluation
• Cost optimization
• Verification loops
• LangGraph workflows
• Production AI architecture

Project Title

AuctionRouter: A Cost-Aware Multi-Agent LLM Routing System with Verification-Based Escalation